Time-Adaptive Fractional Spiking Network for Irregular Multivariate Time Series Forecasting

Irregular multivariate time series (IMTS) forecasting is challenging due to irregular sampling, variable asynchronicity, and partial observability. Most existing methods for IMTS modeling primarily rely on temporal alignment or structural reformulation to alleviate the computational difficulties caused by irregular sampling. However, these methods often fail to fully exploit the sparse event characteristics arising from the partial observability of IMTS. With event-driven computation and sparse activation, spiking neural networks (SNNs) provide a natural modeling paradigm for preserving the event semantics of IMTS and capturing their temporal dependencies. Thus, we propose the Time-Adaptive Fractional Spiking Network (TAFNet), a multi-granularity framework for IMTS forecasting. We introduce a time-adaptive fractional leaky integrate-and-fire (TAF-LIF) neuron that jointly incorporates actual inter-observation intervals and a fractional memory kernel into the membrane potential evolution, enabling interval-aware temporal modeling and persistent historical memory. We further construct multi-granularity temporal representations to capture scale-dependent dynamics under varying observation densities, and integrate them with global periodic information extracted directly from irregular timestamps. Extensive experiments on real-world datasets demonstrate the state-of-the-art performance of TAFNet.